Overlap Syndrome Involving Systemic Lupus Erythematosus and Autoimmune Hepatitis in Children: A Case Report and Literature Review
Bibliographic record
Abstract
Background: The diagnosis of overlap syndrome involving systemic lupus erythematosus (SLE) and autoimmune hepatitis (AIH) is not easily established because of its similar clinical presentations and biochemical features to those of lupus hepatitis. The term overlap syndrome is usually used in the context of overlap of autoimmune hepatitis with PSC (primary sclerosing cholangitis) or PBC (primary biliary cholangitis). Few cases of AIH complicated by SLE have been reported in the literature, and the condition is even rarer in childhood. Case presentation: Here we report the case of a 16-year-old girl with SLE who initially presented with autoimmune (cholestatic) hepatitis. According to American Association for the Study of Liver Diseases practice guidelines, the diagnosis was made based on aggregated scores including female (+2); ALP:AST (or ALT) ratio 1:80 (+3); negative hepatitis viral markers and drug history (+3, +1); average alcohol intake < 25 g/day (+2); and histological interface hepatitis features (+3). She then developed a malar rash, ANA positivity, anti-double-stranded DNA (anti-dsDNA) antibodies, and a low complement level. She met 4 of 17 Systemic Lupus International Collaborating Clinics classification criteria (1) for SLE. Our patient responded very well to corticosteroid at an initial dose of methylprednisolone 40 mg Q12H for 4 days tapering to 1 mg/kg/day according to liver function test results and bilirubin level. No relapse occurred during the 3-year follow-up course. Conclusions: Overlapping of SLE and AIH should be suspected when children with SLE have impaired liver function or AIH patients present with a malar or other skin rash. Liver biopsy plays an important role in establishing the differential diagnosis of SLE with liver impairment or overlap with AIH. The prompt diagnosis and adequate further treatment plans can improve disease outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".